Gravity model in dockless bike-sharing systems within cities

MWTP: A heterogeneous multiplex representation learning framework for link prediction of weak ties

Weak ties that bridge different communities are crucial for preserving global connectivity, enhancing resilience, and maintaining functionality and dynamics of complex networks, However, making accurate link predictions for weak ties remain challenging due to lacking of common neighbors. Most complex systems, such as transportation and social networks, comprise multiple types of interactions, which can be modeled by multiplex networks with each layer representing a different type of connection. Better utilizing information from other layers can mitigate the lack of information for predicting weak ties. Here, we propose a GNN-based representation learning framework for Multiplex Weak Tie Prediction (MWTP). It leverages both an intra-layer and an inter-layer aggregator to effectively learn and fuse information across different layers. The intra-layer one integrates features from multi-order neighbors, and the inter-layer aggregation exploits either logit regression or a more sophisticated semantic voting mechanism to compute nodal-level inter-layer attentions, leading to two variants of our framework, MWTP-logit, and MWTP-semantic. The former one is more efficient in implementation attribute to fewer parameters, while the latter one is slower but has stronger learning capabilities. Extensive experiments demonstrate that our MWTPs outperform eleven popular baselines for predicting both weak ties and all ties across diverse real-world multiplex networks. Additionally, MWTPs achieve good prediction performance with a relatively small training size.

Revealing spatiotemporal interaction patterns behind complex cities

Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing general collective patterns behind spatiotemporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatiotemporal co-occurrence of individuals. The rank-size distributions of dynamic population of locations in all unit time windows are stable, although people are almost constantly moving in cities and hot-spots that attract people are changing over time in a day. A larger city is of a stronger heterogeneity as indicated by a larger scaling exponent. After aggregating spatiotemporal interaction networks over consecutive time windows, we reveal a switching behavior of cities between two states. During the "active" state, the whole city is concentrated in fewer larger communities, while in the "inactive" state, people are scattered in smaller communities. Above discoveries are universal over three cities across continents. In addition, a city stays in an active state for a longer time when its population grows larger. Spatiotemporal interaction segregation can be well approximated by residential patterns only in smaller cities. In addition, we propose a temporal-population-weighted-opportunity model by integrating a time-dependent departure probability to make dynamic predictions on human mobility, which can reasonably well explain the observed patterns of spatiotemporal interactions in cities.

Using the Extended Acceptance Model to Understand Continuance Intention of Dockless Bike-Sharing

Despite the fact that dockless bike-sharing (DBS) usage first experienced explosive growth, its continuous usage rate remains low. The ultimate success of a DBS service is more dependent on its continued usage rather than its initial adoption. Following the extended technology acceptance model (TAM), this study aims to explore factors that influence the continuance intention of DBS users. The framework of research was validated using a sample of 369 DBS users in China. The results show that perceived usefulness and perceived ease of use positively influence a user’s intention to continue using DBS. Both descriptive social norms and injunctive social norms are positively related to the continuance intention of DBS users. Moreover, environmental concern significantly affects the continuance intention of a user indirectly via perceived usefulness and perceived ease of use. Furthermore, the extended TAM has stronger prediction ability than the original TAM in the context of DBS services.